Last Updated on August 27, 2020 Keras is a simple and powerful Python library for deep learning. Given that deep learning models can take hours, days and even weeks to train, it is important to know how to save and load them from disk. In this post, you will discover how you can save your Keras models to file and load them up again to make predictions. After reading this tutorial you will know: How to save model weights and model […]

Last Updated on September 15, 2020 Keras is a powerful and easy-to-use free open source Python library for developing and evaluating deep learning models. It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in just a few lines of code. In this tutorial, you will discover how to create your first deep learning neural network model in Python using Keras. Kick-start your project with my new book Deep Learning […]

Last Updated on August 14, 2020 Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. If you are just starting out in the field of deep learning or you had some experience with neural networks some time ago, you may be confused. I know I was confused initially and so were many of my colleagues and friends who learned and used neural networks in the 1990s […]

Last Updated on August 27, 2020 Machine learning and deep learning models, like those in Keras, require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an integer encoding and a one hot encoding, although a newer technique called learned embedding may provide a useful middle ground between these two methods. In […]

Last Updated on August 27, 2020 Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep learning framework developed and maintained by Google. Although using TensorFlow directly can be challenging, the modern tf.keras API beings the simplicity and ease of use of Keras to the TensorFlow project. Using tf.keras allows you to design, fit, evaluate, and use deep learning models to make predictions in just a few lines of code. […]

Last Updated on August 27, 2020 Supervised learning in machine learning can be described in terms of function approximation. Given a dataset comprised of inputs and outputs, we assume that there is an unknown underlying function that is consistent in mapping inputs to outputs in the target domain and resulted in the dataset. We then use supervised learning algorithms to approximate this function. Neural networks are an example of a supervised machine learning algorithm that is perhaps best understood in […]

Last Updated on August 27, 2020 Predictive modeling with deep learning is a skill that modern developers need to know. PyTorch is the premier open-source deep learning framework developed and maintained by Facebook. At its core, PyTorch is a mathematical library that allows you to perform efficient computation and automatic differentiation on graph-based models. Achieving this directly is challenging, although thankfully, the modern PyTorch API provides classes and idioms that allow you to easily develop a suite of deep learning […]

Last Updated on August 28, 2020 Multi-output regression involves predicting two or more numerical variables. Unlike normal regression where a single value is predicted for each sample, multi-output regression requires specialized machine learning algorithms that support outputting multiple variables for each prediction. Deep learning neural networks are an example of an algorithm that natively supports multi-output regression problems. Neural network models for multi-output regression tasks can be easily defined and evaluated using the Keras deep learning library. In this tutorial, […]

Last Updated on August 31, 2020 Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.” Deep learning neural networks are an example of an algorithm that natively supports multi-label classification problems. Neural network models for multi-label classification tasks can be easily defined and evaluated using the Keras deep learning library. In this tutorial, you […]

Last Updated on September 6, 2020 AutoML refers to techniques for automatically discovering the best-performing model for a given dataset. When applied to neural networks, this involves both discovering the model architecture and the hyperparameters used to train the model, generally referred to as neural architecture search. AutoKeras is an open-source library for performing AutoML for deep learning models. The search is performed using so-called Keras models via the TensorFlow tf.keras API. It provides a simple and effective approach for […]